{"id":"W2004793341","doi":"10.1118/1.1617411","title":"Inverse treatment planning by physically constrained minimization of a biological objective function","year":2003,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal General Hospital; McGill University Health Centre; University of Alberta","funders":"","keywords":"Mathematical optimization; Radiation treatment planning; Minification; Penalty method; Inverse; Computer science; Function (biology); Inverse problem; Constrained optimization; Mathematics; Radiation therapy; Medicine; Surgery","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007396967,0.0006193842,0.000621484,0.0004714174,0.0002341624,0.0006089523,0.0007048848,0.0007831995,0.00131137],"category_scores_gemma":[0.001345104,0.0004830417,0.0006618365,0.0004454657,0.0007976293,0.0005830517,0.0007952815,0.0006854835,0.0003144841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005720935,"about_ca_system_score_gemma":0.0009357593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001022984,"about_ca_topic_score_gemma":0.001088557,"domain_scores_codex":[0.9994878,0.0001520652,0.00001773801,0.00005600466,0.0002683342,0.0000181238],"domain_scores_gemma":[0.9996756,0.0001808249,0.00004906271,0.0000366976,0.00004697026,0.00001082682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003361908,0.00002304991,0.0001300138,0.00008091036,0.00002593321,0.00003799559,0.00003202373,0.944676,0.01340818,0.0135516,0.0004566995,0.02754394],"study_design_scores_gemma":[0.00001502809,0.00003670923,0.0001394101,0.000008636046,0.000008460219,0.00006520369,0.000005856161,0.9877139,0.003166373,0.00698759,0.001840512,0.00001233706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002664066,0.00007067589,0.995987,0.00005084466,0.000008328422,0.0000218237,0.00001107407,0.00008055138,0.001105674],"genre_scores_gemma":[0.1818147,0.0002008133,0.8147246,0.0001146588,0.00003080659,0.0003572016,0.0001010759,0.0001680792,0.002487958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00131137,"threshold_uncertainty_score":0.004386961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01632313314594555,"score_gpt":0.2794475387780531,"score_spread":0.2631244056321076,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}